Learning from Your App’s Active Users for Better Marketing Outcomes

You’ve established an engaged user base for your app. Do you ever wish you could just clone these ideal app users—the ones who not only downloaded your app but continually engage over time? Well, there’s good news: You can learn from their mobile usage habits and characteristics in order to refine your future advertising campaigns. Marketers are already sitting on a wealth of valuable information about who uses their app and how they do so; they just have to put it to use for better marketing outcomes.

Part of this process entails using dynamic ads and posting them across mobile ad networks for marketing apps, of course. But not all impressions are worth the same amount of ad spend. Savvy marketers understand the benefits of targeting people who look and act like their existing engaged audience because these lookalike audiences have a higher likelihood of interacting with your app over time.

Modern machine-learning technology can assist in “prospecting” for high-quality users by “holistically analyzing multiple layers of behavioral activity to streamline potential customer segments.” In other words, sifting through mobile users at large to find profiles that match elements of your target audience helps you serve ads to those statistically more likely to be interested in your app. The idea is that ad spend becomes more strategic and ROI becomes more favorable as you continue to refine your campaigns.

What kind of characteristics are useful in determining who’s a likely high-quality user? There are hundreds of demographic and usage markers that make up mobile profiles, including:

Age

Location

Gender

Type of device

Other apps installed on device

Previous interactions with ads

Drilling down further into these audience segments helps marketers create helpful mobile user profiles. For example, based on your most engaged audience, you may find it’s worthwhile to serve ads for your social-networking app to 28-year-old female professionals living in cities with populations over 1,000,000 who have iPhones with the LinkedIn mobile app also installed. Someone marketing a midcore gaming app could have better luck with 20-year-old males living in college towns who use the Android operating system. While these are simplified examples, it just goes to show that you have to know your audience to grow your audience.

Machine-learning tools also alleviate the need for marketers to manually design and place ads by using AI and real-time bidding to do it programmatically. Dynamic ads take the desired audience characteristics and incorporate them into the ad for a more personalized experience for mobile app users. After all, post-install conversion rates go up when ads really appeal to the individuals viewing them. More relevant ads mean more downloads, and, more importantly, higher post-install engagement from users.

Want to utilize a database of over two billion mobile profiles containing more than 200 user characteristics for more impactful app marketing campaigns? Discover what Liftoff’s mobile app marketing platform can do for you today.